Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 221 Longwood Ave, Boston MA 02115, room 225C, USA.
EBioMedicine. 2021 Jun;68:103433. doi: 10.1016/j.ebiom.2021.103433. Epub 2021 Jun 15.
Multiple aspects of sleep and Sleep Disordered Breathing (SDB) have been linked to hypertension. However, the standard measure of SDB, the Apnoea Hypopnea Index (AHI), has not identified patients likely to experience large improvements in blood pressure with SDB treatment.
To use machine learning to select sleep and pulmonary measures associated with hypertension development when considered jointly, we applied feature screening followed by Elastic Net penalized regression in association with incident hypertension using a wide array of polysomnography measures, and lung function, derived for the Sleep Heart Health Study (SHHS).
At baseline, n=860 SHHS individuals with complete data were age 61 years, on average. Of these, 291 developed hypertension ~5 years later. A combination of pulmonary function and 18 sleep phenotypes predicted incident hypertension (OR=1.43, 95% confidence interval [1.14, 1.80] per 1 standard deviation (SD) of the phenotype), while the apnoea-hypopnea index (AHI) had low evidence of association with incident hypertension (OR =1.13, 95% confidence interval [0.97, 1.33] per 1 SD). In a generalization analysis in 923 individuals from the Multi-Ethnic Study of Atherosclerosis, aged 65 on average with 615 individuals with hypertension, the new phenotype was cross-sectionally associated with hypertension (OR=1.26, 95% CI [1.10, 1.45]).
A unique combination of sleep and pulmonary function measures better predicts hypertension compared to the AHI. The composite measure included indices capturing apnoea and hypopnea event durations, with shorter event lengths associated with increased risk of hypertension.
This research was supported by National Heart, Lung, and Blood Institute (NHLBI) contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 and by National Center for Advancing Translational Sciences grants UL1-TR- 000040, UL1-TR-001079, and UL1-TR-001420. The MESA Sleep ancillary study was supported by NHLBI grant HL-56984. Pulmonary phenotyping in MESA was funded by NHLBI grants R01-HL077612 and R01-HL093081. This work was supported by NHLBI grant R35HL135818 to Susan Redline.
睡眠和睡眠呼吸紊乱（SDB）的多个方面与高血压有关。然而，SDB 的标准衡量标准——呼吸暂停低通气指数（AHI），并没有确定哪些患者可能会因 SDB 治疗而使血压得到显著改善。
为了使用机器学习来选择与高血压发展相关的睡眠和肺部措施，当联合考虑时，我们应用特征筛选，然后应用弹性网络惩罚回归，结合广泛的多导睡眠图测量和源自睡眠心脏健康研究（SHHS）的肺功能，对高血压进行关联分析。
在基线时，n=860 名 SHHS 个体具有完整数据，平均年龄为 61 岁。其中，291 人在大约 5 年后患上了高血压。肺部功能和 18 种睡眠表型的组合预测了高血压的发生（每个表型的标准差为 1 时，比值比[OR]为 1.43，95%置信区间[1.14，1.80]），而呼吸暂停-低通气指数（AHI）与高血压的发生仅有低证据相关性（每个 AHI 的标准差为 1 时，OR=1.13，95%置信区间[0.97，1.33]）。在多民族动脉粥样硬化研究中 923 名平均年龄为 65 岁的个体中的推广分析中，有 615 名个体患有高血压，新表型与高血压呈横断面相关（OR=1.26，95%置信区间[1.10，1.45]）。
与 AHI 相比，睡眠和肺部功能的独特组合能更好地预测高血压。综合指标包括捕捉呼吸暂停和低通气事件持续时间的指标，事件持续时间越短，患高血压的风险就越高。
这项研究得到了美国国立心肺血液研究所（NHLBI）合同 HHSN268201500003I、N01-HC-95159、N01-HC-95160、N01-HC-95161、N01-HC-95162、N01-HC-95163、N01-HC-95164、N01-HC-95165、N01-HC-95166、N01-HC-95167、N01-HC-95168 和 N01-HC-95169 的支持，并得到了美国国立卫生研究院转化科学推进中心授予的 UL1-TR-000040、UL1-TR-001079 和 UL1-TR-001420 号赠款的支持。MESA 睡眠辅助研究得到了 NHLBI 授予的 HL-56984 号赠款的支持。MESA 的肺部表型研究得到了 NHLBI 授予的 R01-HL077612 和 R01-HL093081 号赠款的支持。这项工作得到了 NHLBI 授予的 R35HL135818 号赠款给 Susan Redline 的支持。
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